Role Description - Core Ab Initio Components & Skills - GDE (Graphical Development Environment): - Creating, testing, and debugging ETL graphs. - Co>Operating System: - Core execution engine handling parallel processing, checkpointing, and recovery. - EME (Enterprise Meta>Environment): - Managing technical metadata, lineage, and version control. - Parallelism: - Component, Data, and Pipeline parallelism for high-volume processing. - Continuous Flows: - Real-time data processing and messaging queue integration. - Data Manipulation Language (DML): - Defining data structures and complex transformation logic. - Transform Components: - Utilizing Rollup, Join, Scan, and Reformat for data manipulation. Company Description At Zensar, we’re “experience-led everything”. We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus. Part of the $4.8 billion RPG Group, we’re a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.

Role Description We’re looking for an Applied Scientist to join our Consumer org. In this role you’ll work on some of the most technically challenging ML problems across DoorDash: understanding what our customers are looking for, identifying key concepts in their queries and surfacing the most relevant results. You'll be embedded in a cross-disciplinary team of engineers, ML engineers and applied scientists with full ownership from research to production. If your expertise matches our domain and you want to work on hard problems at global scale alongside exceptional colleagues, we'd love to meet you. What you’ll be doing - Design and develop ML models for search relevance, query understanding, and ranking that operate across DoorDash, Deliveroo, and Wolt’s 40+ markets. - Bring state-of-the-art solutions to our stack for the delight of our customers, helping the team to impact business metrics. - Work end-to-end on ML problems: from problem framing and data analysis through model development, offline evaluation, and production monitoring. - Collaborate with Software Engineers, ML Engineers, Product Managers, and Analysts to translate research insights into real customer impact. - Contribute to our group-wide Applied Science community through knowledge sharing, technical reviews, and raising the bar on ML practices. Qualifications - You have 4+ years of hands-on experience in applied ML with a track record of shipping models to production (a PhD in ML with applied research experience is equally welcome). - You have solid experience in Search: query understanding, query intent prediction, or semantic search. - You are proficient in Python and experienced with ML frameworks and large-scale data processing. - You communicate complex technical ideas clearly and collaborate effectively with cross-functional teams. - Experience with NLP, dense retrieval, learning-to-rank, or embedding-based methods is a strong plus. Benefits - Direct and measurable impact on millions of customers every day. - Join a team of world-class applied scientists and engineers in DoorDash, Deliveroo, and Wolt. - Opportunity to create a personalised development plan to grow your strengths and to develop new capabilities. - Relocation support to help you join us. Next steps - TA Screen: a 30-minute introductory call with one of our Talent Partners to learn about your background and tell you more about the role. - Hiring Manager interview: a conversation covering your domain experience in Search or Personalisation, past projects, AI fluency, and ways of working. - Coding & System Design: a technical session focused on ML system design and problem-solving with some of the team’s ML Engineers. - Project / Expertise Deep Dive: an interview with the team’s Applied Scientists, going deep on your domain expertise and past projects. Our Commitment to Diversity and Inclusion We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

• Plan, coordinate, and supervise all activities related to the design development, and implementation of organizational reporting and analytics • Maintain, support, and upgrade reports, dashboards, and data warehouse • Apply proven communication skills, problem solving skills, and knowledge of best practices to guide the team on issues related to the deployment of information and reports • Managing data warehouse and reporting infrastructure to help scale reporting needs while maintaining high-performance environment for reporting • Audit to ensure accuracy of financial data • Contribute to team effort by accomplishing related results as needed • Protect the organization's value by keeping information confidential

• Creating new and improving existing AI solutions Involvement in the whole process of data-driven development from analyzing product requirements to launching the feature or system to production (with the help of MLOps specialists) and improving it based on results of monitoring and feedback • Communicating with fellow data scientists, product owners, and developers • Applying best software engineering practices to build a reliable codebase • Performing code reviews • Working in agile environment and presenting the results of your work on regular sprint reviews